{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring photon heights with ICESat-2 (ATL03)\n",
    "\n",
    "Information obtained primarily from the ATL03 Algorithm Theoretical Basis Document (ATBD, Neumann et al., 2019) and the NSIDC product description page: https://nsidc.org/data/atl03.   \n",
    "\n",
    "* Notebook author: Alek Petty (relying extensively on the ATBD and product description)    \n",
    "* Description: Notebook describing the ICESat-2 ATL03 product.   \n",
    "* Input requirements: Any example ATL03 data file.   \n",
    "* Date: June 2019\n",
    "* More info: See the ATL03 Algorithm Theoretical Basis Document (ATBD): https://icesat-2.gsfc.nasa.gov/sites/default/files/page_files/ICESat2_ATL03_ATBD_r001.pdf and the known issues document: https://nsidc.org/sites/nsidc.org/files/technical-references/ATL03_Known_Issues_May2019.pdf\n",
    "\n",
    "## Notebook objectives\n",
    "* General understanding of the data included in a typical ATL03 file.\n",
    "* Reading in, plotting and basic analysis of ATL03 data.\n",
    "* What we can learn from ATL03 to derive the ATL07 surface height segments!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook instructions\n",
    "1. Follow along with the notebook tutorial. \n",
    "2. Play around changing options and re-running the relevant notebook cells. \n",
    "\n",
    "Here I use the HDF5 ATL03 file (ATL03_20181115022655_07250104_001_01.h5) obtained from: https://nsidc.org/data/atl03. See the NSIDC tutorial for more information on generating granules of data to use in this tutorial. If using this using the ICESat-2 Pangeo instance, you can download the file from Amazon S3 using the notebook cell provided below.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ATL03 Background\n",
    "\n",
    "*NB: This is my laymans description of ATL03 compiled from the above resources, trying to condense this information to the key points of interest to potential sea ice users. let me know if you see any errors!*\n",
    "\n",
    "ATL03 is a Level 2A ICESat-2 data product available from the NSIDC: https://nsidc.org/data/atl03. It's assumed that ATL03 will be the lowest level product of interest to sea ice users of ICESat-2 data. ATL03 is based on the ICESat-2 Level 1B data product (ATL02) which provides the precise round-trip time of flight of individual photons.  ATL03 combines this information with the  the observatory position and the laser pointing vectors to estimate the location of the photon surface returns relative to the WGS-84 ellipsoid. The ATL03 product is then used by the higher level (Level 3) products that provide more focussed geophysical datasets to respective user communities (e.g. ATL07 below for sea ice).\n",
    "\n",
    "The data within a given ATL03 fie includes individual photons heights, background rates, and corrections applied to the height estimates. ATL03 applies multiple geophysical corrections to the height estimates to generate a best estimate of the photon height. \n",
    "\n",
    "Additional corrections that some users may decide to apply are provided within the product. A number of meteorological parameters (e.g., wind, surface air temperature, sea level pressure, etc. from reanalyses) are also provided with ATL03. ATL03 files are big (serveral hundreds of Gbs for a given granule, hence the desire to produce and release higher level processed products to the community).\n",
    "\n",
    "ATL03 provides a coarse surface classification to enable higher-level products to easily grab the photons of interest from a given granule (land, ocean, sea ice, land ice, and inland water areas, multiple categories permitted).  \n",
    "\n",
    "The product also attempts to discriminate between signal (surface returns) and background photon events. The background photons are mainly a result of solar photons at the same frequency of the ATLAS detector (532 nm) that have scattered from clouds or the surface. The background is thus expected to drop to near-zero during nighttime. A background photon rate is calculated by expanding the vertical range of photon detection to over 10 km for more than 400 transmitted laser pulses from the strong beams. Form this expected background rate, a photon rate threhold is set to search for signal photons within vertical elevation bins. The photons are classified for the given surface type classification based on the signal-to-noise ratio: high-confidence signal (SNR > 100), medium-confidence signal (100 > SNR > 40), low confidence signal (40 > SNR > 3), or likely background (SNR < 3).  \n",
    "\n",
    "Finally, several geophysical corrections are applied to these WGS84 relative heights, including:\n",
    "* the height of the ocean pole tide\n",
    "* the height of the ocean load tide\n",
    "* the height of the solid earth pole tide\n",
    "* the solid earth tide\n",
    "* the height of the total column atmospheric delay correction.  \n",
    "\n",
    "The data product also includes (but doesn't use) the geoid (EGM2008), the ocean tide (GOT4.8) and the AVISO dynamic atmospheric correction (MOG2D) which are often used by the higher level products like ATL07.\n",
    "\n",
    "Below is an example of how one can read in and explore a given ATL03 file. Very open to suggestions on possible improvements!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Magic function to enable interactive plotting in Jupyter notebook\n",
    "#Allows you to zoom/pan within plots after generating\n",
    "#Normally, this would be %matplotlib notebook, but since we're using Juptyerlab, we need a different widget\n",
    "#%matplotlib notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "#Import necesary modules\n",
    "#Use shorter names (np, pd, plt) instead of full (numpy, pandas, matplotlib.pylot) for convenience\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "#import seaborn as sns\n",
    "import pandas as pd\n",
    "import h5py  \n",
    "import s3fs\n",
    "import readers as rd\n",
    "import utils as ut\n",
    "# Use seaborn for nicer looking inline plots\n",
    "#sns.set(context='notebook', style='darkgrid')\n",
    "#st = axes_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Beam selection   \n",
    "There are 6 beams to choose from in the ICESat-2 products (3 pairs of a strong and weak beam). The energy ratio between the weak and strong beams are  approximately 1:4 and are separated by 90 m in the across-track direction. The beam pairs are separated by ~3.3 km in the across-track direction, and the strong and weak beams are separated by ~2.5 km in the along-track direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "beamStr='gt1r'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data selection    \n",
    "If running on Pangeo instance you can greab data directly from Amazon S3 like so\n",
    "Comment out the last command if you've already got the data in the Data dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pangeo-data-upload-oregon/icesat2/ATL03_20181115022655_07250104_001_01.h5', 'pangeo-data-upload-oregon/icesat2/ATL07-01_20181115003141_07240101_001_01.h5', 'pangeo-data-upload-oregon/icesat2/ATL10-01_20181115003141_07240101_001_01.h5', 'pangeo-data-upload-oregon/icesat2/readme.txt', 'pangeo-data-upload-oregon/icesat2/Clouds_and_filtering_tutorial', 'pangeo-data-upload-oregon/icesat2/Floes-are-Swell', 'pangeo-data-upload-oregon/icesat2/Snowblower', 'pangeo-data-upload-oregon/icesat2/atl03', 'pangeo-data-upload-oregon/icesat2/atl06', 'pangeo-data-upload-oregon/icesat2/atl10', 'pangeo-data-upload-oregon/icesat2/data-access-outputs', 'pangeo-data-upload-oregon/icesat2/data-access-shapefile', 'pangeo-data-upload-oregon/icesat2/data-access-subsetted-outputs', 'pangeo-data-upload-oregon/icesat2/data-access-subsettedoutputs', 'pangeo-data-upload-oregon/icesat2/data-correction', 'pangeo-data-upload-oregon/icesat2/elevation_change_tutorial', 'pangeo-data-upload-oregon/icesat2/geospatial-analysis', 'pangeo-data-upload-oregon/icesat2/glaciersat2', 'pangeo-data-upload-oregon/icesat2/gridding-data', 'pangeo-data-upload-oregon/icesat2/ground2float', 'pangeo-data-upload-oregon/icesat2/juneauicefield', 'pangeo-data-upload-oregon/icesat2/pine_island_glims', 'pangeo-data-upload-oregon/icesat2/segtrax', 'pangeo-data-upload-oregon/icesat2/xtrak']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'../Data/ATL03_20181115022655_07250104_001_01.h5'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bucket = 'pangeo-data-upload-oregon'\n",
    "fs = s3fs.S3FileSystem()\n",
    "dataDir = 'pangeo-data-upload-oregon/icesat2/'\n",
    "s3List = fs.ls(dataDir)\n",
    "print(s3List)\n",
    "ATL03file='ATL03_20181115022655_07250104_001_01.h5'\n",
    "s3File='pangeo-data-upload-oregon/icesat2/'+ATL03file\n",
    "localFilePath='../Data/'+ATL03file\n",
    "#fs.get(s3File, localFilePath)\n",
    "localFilePath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If data file is stored locally (already grabbed from S3) uncomment the following lines and comment out the below S3 example..\n",
    "#file_path = '../Data/'\n",
    "#ATL03_filename = 'ATL03_20181115022655_07250104_001_01.h5'\n",
    "#localPath = file_path + ATL03_filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getATL03data(fileT, numpyout=False, beam='gt1l'):\n",
    "    \"\"\" Pandas/numpy ATL03 reader\n",
    "    Written by Alek Petty, June 2018 (alek.a.petty@nasa.gov)\n",
    "\n",
    "    I've picked out the variables from ATL03 I think are of most interest to sea ice users, but by no\n",
    "    means is this an exhastive list. \n",
    "    See the xarray or dictionary readers to load in the more complete ATL03 dataset\n",
    "    or explore the hdf5 files themselves (I like using the app Panpoly for this) to see what else you\n",
    "    might want\n",
    "    \n",
    "    Args:\n",
    "        fileT (str): File path of the ATL03 dataset\n",
    "        numpy (flag): Binary flag for outputting numpy arrays (True) or pandas dataframe (False)\n",
    "        beam (str): ICESat-2 beam (the number is the pair, r=strong, l=weak)\n",
    "        \n",
    "    returns:\n",
    "        either: select numpy arrays or a pandas dataframe\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    # Open the file\n",
    "    try:\n",
    "        ATL03 = h5py.File(fileT, 'r')\n",
    "    except:\n",
    "        'Not a valid file'\n",
    "        \n",
    "    lons=ATL03[beam+'/heights/lon_ph'][:]\n",
    "    lats=ATL03[beam+'/heights/lat_ph'][:]\n",
    "    \n",
    "    #  Number of seconds since the GPS epoch on midnight Jan. 6, 1980 \n",
    "    delta_time=ATL03[beam+'/heights/delta_time'][:] \n",
    "    \n",
    "    # #Add this value to delta time parameters to compute the full gps_seconds\n",
    "    atlas_epoch=ATL03['/ancillary_data/atlas_sdp_gps_epoch'][:] \n",
    "    \n",
    "    # Conversion of delta_time to a calendar date\n",
    "    # This function seems pretty convoluted but it works for now..\n",
    "    # Sure there is a simpler functionw e can use here instead.\n",
    "    temp = ut.convert_GPS_time(atlas_epoch[0] + delta_time, OFFSET=0.0)\n",
    "    \n",
    "    # Express delta_time relative to start time of granule\n",
    "    delta_time_granule=delta_time-delta_time[0]\n",
    "\n",
    "    year = temp['year'][:].astype('int')\n",
    "    month = temp['month'][:].astype('int')\n",
    "    day = temp['day'][:].astype('int')\n",
    "    hour = temp['hour'][:].astype('int')\n",
    "    minute = temp['minute'][:].astype('int')\n",
    "    second = temp['second'][:].astype('int')\n",
    "    \n",
    "    dFtime=pd.DataFrame({'year':year, 'month':month, 'day':day, \n",
    "                        'hour':hour, 'minute':minute, 'second':second})\n",
    "    \n",
    "    \n",
    "    # Primary variables of interest\n",
    "    \n",
    "    # Photon height\n",
    "    heights=ATL03[beam+'/heights/h_ph'][:]\n",
    "    print(heights.shape)\n",
    "    \n",
    "    # Flag for signal confidence\n",
    "    # column index:  0=Land; 1=Ocean; 2=SeaIce; 3=LandIce; 4=InlandWater\n",
    "    # values:\n",
    "        #-- -1: Events not associated with a specific surface type\n",
    "        #--  0: noise\n",
    "        #--  1: buffer but algorithm classifies as background\n",
    "        #--  2: low\n",
    "        #--  3: medium\n",
    "        #--  4: high\n",
    "    signal_confidence=ATL03[beam+'/heights/signal_conf_ph'][:,2] \n",
    "    \n",
    "    # Add photon rate, background rate etc to the reader here if we want\n",
    "    \n",
    "    ATL03.close()\n",
    "    \n",
    "    \n",
    "    \n",
    "    dF = pd.DataFrame({'heights':heights, 'lons':lons, 'lats':lats,\n",
    "                       'signal_confidence':signal_confidence, \n",
    "                       'delta_time':delta_time_granule})\n",
    "    \n",
    "    # Add the datetime string\n",
    "    dFtimepd=pd.to_datetime(dFtime)\n",
    "    dF['datetime'] = pd.Series(dFtimepd, index=dF.index)\n",
    "    \n",
    "    # Filter out high elevation values \n",
    "    #dF = dF[(dF['signal_confidence']>2)]\n",
    "    # Reset row indexing\n",
    "    #dF=dF.reset_index(drop=True)\n",
    "    return dF\n",
    "    \n",
    "    # Or return as numpy arrays \n",
    "    # return along_track_distance, heights\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read in the data using the pandas reader above. Copied from the readers.py script.\n",
    "\n",
    "Take a look at the top few rows (change the number in head to increase this..)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4699046,)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>heights</th>\n",
       "      <th>lons</th>\n",
       "      <th>lats</th>\n",
       "      <th>signal_confidence</th>\n",
       "      <th>delta_time</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.656945</td>\n",
       "      <td>162.729396</td>\n",
       "      <td>79.995289</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2018-11-15 02:26:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.911671</td>\n",
       "      <td>162.729396</td>\n",
       "      <td>79.995289</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2018-11-15 02:26:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.856270</td>\n",
       "      <td>162.729388</td>\n",
       "      <td>79.995295</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>2018-11-15 02:26:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.656709</td>\n",
       "      <td>162.729380</td>\n",
       "      <td>79.995301</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>2018-11-15 02:26:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.688022</td>\n",
       "      <td>162.729380</td>\n",
       "      <td>79.995301</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>2018-11-15 02:26:54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    heights        lons       lats  signal_confidence  delta_time  \\\n",
       "0  0.656945  162.729396  79.995289                  4      0.0000   \n",
       "1  0.911671  162.729396  79.995289                  4      0.0000   \n",
       "2  0.856270  162.729388  79.995295                  4      0.0001   \n",
       "3  0.656709  162.729380  79.995301                  4      0.0002   \n",
       "4  0.688022  162.729380  79.995301                  4      0.0002   \n",
       "\n",
       "             datetime  \n",
       "0 2018-11-15 02:26:54  \n",
       "1 2018-11-15 02:26:54  \n",
       "2 2018-11-15 02:26:54  \n",
       "3 2018-11-15 02:26:54  \n",
       "4 2018-11-15 02:26:54  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dF03= getATL03data(localFilePath, beamStr)\n",
    "dF03.head(5)\n",
    "\n",
    "# Other readers available in readers.py script"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Map the data for visual inspection using Cartopy \n",
    "*NB (Basemap is often used for mapping but is not being officially supported by the community anymore)*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 630x630 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate a shorted version for mapping purposes\n",
    "dF03short=dF03.iloc[::1000, :]\n",
    "dF03short.head(5)\n",
    "\n",
    "var='heights'\n",
    "\n",
    "plt.figure(figsize=(7,7), dpi= 90)\n",
    "# Make a new \"NorthPolarStereo\" projection instance\n",
    "ax = plt.axes(projection=ccrs.NorthPolarStereo(true_scale_latitude=70))\n",
    "plt.scatter(dF03short['lons'], dF03short['lats'],c=dF03short[var], cmap='viridis', transform=ccrs.PlateCarree())\n",
    "\n",
    "ax.coastlines()\n",
    "#ax.drawmeridians()\n",
    "plt.colorbar(label=var, shrink=0.5, extend='both')\n",
    "\n",
    "# Limit the map to -60 degrees latitude and below.\n",
    "ax.set_extent([-180, 180, 90, 60], ccrs.PlateCarree())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the photon heights of this section\n",
    "*NB: Could use seaborn for this (for one line plots) but using matplotlib for increased user flexibility.*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.plot(dF03['datetime'],dF03['heights'], color='k', marker='.', linestyle='None', alpha=0.3)\n",
    "#plt.xlabel('Delta time (s)')\n",
    "plt.ylabel('Photon heights (m)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the photon heights but now use the signal confidence to label the markers.\n",
    "\n",
    "#np.size(np.where(dF03['signal_confidence']==3))\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.plot(dF03[(dF03['signal_confidence']<2)]['delta_time'],dF03[(dF03['signal_confidence']<2)]['heights'], label='other', color='g', marker='.', linestyle='None', alpha=0.3)\n",
    "plt.plot(dF03[(dF03['signal_confidence']==2)]['delta_time'],dF03[(dF03['signal_confidence']==2)]['heights'], label='low', color='b', marker='.', linestyle='None', alpha=0.3)\n",
    "plt.plot(dF03[(dF03['signal_confidence']==3)]['delta_time'],dF03[(dF03['signal_confidence']==3)]['heights'], label='medium', color='c', marker='.', linestyle='None', alpha=0.1)\n",
    "plt.plot(dF03[(dF03['signal_confidence']==4)]['delta_time'],dF03[(dF03['signal_confidence']==4)]['heights'], label='high', color='m', marker='.', linestyle='None', alpha=0.1)\n",
    "plt.legend(loc=1)\n",
    "plt.xlabel('Delta time (s)')\n",
    "plt.ylabel('Photon heights (m)')\n",
    "plt.show()\n",
    "\n",
    "# Another (more elegant but less flexible) of doing a plot like this is using seaborn...\n",
    "#plt.figure(figsize=(12, 5))\n",
    "#sns.pairplot(x_vars=[\"delta_time\"], y_vars=[\"heights\"], data=dF03, \n",
    "#hue=\"signal_confidence\", size=5)\n",
    "#plt.gca().set_ylim((0, 50000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's do some basic filtering of the data! \n",
    "*Note that for sea ice our task is easier than land ice, as we know our returns should be coming from a narrow window around sea level*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now let's only keep the high confidence photons\n",
    "dF03ice = dF03[(dF03['signal_confidence']>3)]\n",
    "\n",
    "# ...and let's apply a hight window around the surface (+/- 50 m)\n",
    "dF03ice = dF03ice[(abs(dF03ice['heights'])<50)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data like before\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.plot(dF03ice['delta_time'],dF03ice['heights'], label='high confidence surface returns', color='m', marker='.', linestyle='None', alpha=0.3)\n",
    "# plot a rolling mean of the photon heights. 150 photons is used in the generation of ATL07\n",
    "plt.plot(dF03ice['delta_time'].rolling(150).mean(),dF03ice['heights'].rolling(150).mean(), label='rolling mean (150 photon window)', color='k', linestyle='-', alpha=0.3)\n",
    "plt.legend(loc=2, frameon=False)\n",
    "plt.xlabel('Delta time (s)')\n",
    "plt.ylabel('Photon heights (m)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We now need to make extra corrections to the data not included in ATL03 (ocean tides, geoid, DAC?)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Extra ideas\n",
    "\n",
    "1. Check out the surface classification (check it's all sea ice or open water?!)\n",
    "2. Play around removing the corrections and see what that does to the heights.\n",
    "3. ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Onwards to the ATL07 Notebook...!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
